Conventional process analysis software tools utilize general purpose scheduling, critical path method (CPM) modeling, and simulation, which require static and well-defined process knowledge when dealing with the dynamic nature of a process. When analyzing large scale processes, such as airplane or automotive assembly, the dynamic variability of processes requires not only knowledge about inter-dependency among processes and tasks but also a capability for data mining to account for historic performance. However, conventional tools suffer from: 1) a lack of end-to-end real-time process dependency determination and monitoring, 2) taking a knowledge-centric approach, and 3) being less capable of addressing large-scale problems.
Currently there is no holistic solution for process task flow analysis applicable for end-to-end, large-scale, and complex processes. Thus, what is needed is a system and method that enables efficient management of end-to-end processes with a large number of tasks involved. Our approach to managing real-time situation and knowledge extracted from the historic performance provides greater flexibility and reflect the reality of dynamic process requirements.